{"id":"W4389002969","doi":"10.1093/gbe/evad211","title":"Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution","year":2023,"lang":"en","type":"article","venue":"Genome Biology and Evolution","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Phylogenetic tree; Biology; Phylogenetic comparative methods; Divergence (linguistics); Trait; Expression (computer science); Evolutionary biology; Gene; Set (abstract data type); Phylogenetics; Computational biology; Gene expression; Genetics; Statistics; Computer science; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005155433,0.000124616,0.0001414996,0.00004121367,0.0002825256,0.000004362315,0.0001270188,0.0001436946,0.000001029274],"category_scores_gemma":[0.00004282405,0.00009430652,0.00006081741,0.0001028338,0.0001593259,0.000001629484,0.000136221,0.00004101399,0.000002203112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001671464,"about_ca_system_score_gemma":0.00005225093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001226381,"about_ca_topic_score_gemma":0.000005887389,"domain_scores_codex":[0.9990988,0.00007633773,0.0002185919,0.0003016435,0.00005684599,0.0002477339],"domain_scores_gemma":[0.9994879,0.00003683137,0.0001144313,0.0002251058,0.000107192,0.00002854017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001203437,0.000009300474,0.01561209,0.0000241534,0.00003529821,2.227315e-8,0.00009170183,0.01230778,0.9709274,0.0002911791,0.00002780087,0.0005529818],"study_design_scores_gemma":[0.001982288,0.004183441,0.5888575,0.00003007116,0.0001322162,0.00001477608,0.0003644699,0.09573923,0.2929153,0.01420672,0.001026376,0.0005475971],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9866303,0.005126956,0.007570055,0.00006574571,0.0001588431,0.0003606164,0.00005944484,0.000006607431,0.0000214978],"genre_scores_gemma":[0.9974355,0.0007196594,0.001311187,0.00002085302,0.0001872908,0.0001100292,0.0001147394,0.00001227762,0.00008845309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.678012,"threshold_uncertainty_score":0.3845708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04542684295846601,"score_gpt":0.3010901160379307,"score_spread":0.2556632730794647,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}